원문정보
초록
영어
In the present paper analysis of performance parameters i.e., insertion loss and return loss of microstrip Low Pass Filter with open stub using Artificial Neural Networks has been presented. The Artificial neural network is used in predicting the performance parameters of the low pass filter with open stub as a function of its stub length. Levenberg –Marquardt training algorithms of FFBP-ANN. (feed forward back propagation Artificial Neural Network), Layer Recurrent-ANN and CFBP-ANN (cascaded forward back propagation Artificial Neural Network) has been used to implement the neural network models. Simulated values for training and testing the neural network are obtained by analysing the LPF structure by the use of CST Microwave Studio Software. Comparison of mean square error obtained from different ANN networks concluded that CFBP-ANN gives satisfactory result as compare to FFBP-ANN and Layer Recurrent ANN. The testing of output of neural model is found good agreement with simulated output.
목차
1. Introduction
2. Design and Data Generation
3. Ann Models for The Analysis Performance Parameters of Mircrostrip LPF
3.1. Feed Forward Back Propagation (FFBP)Neural Network
3.2. Layer Recurrent Neural Network
3.3. Cascaded Forward Back Propagation (CFBP) Neural Network
4. Training and Testing Through ANN
4.1. Training and Testing for the Analysis of Return Loss Through ANN
4.2. Training and Testing for the Analysis of Insertion Loss Through ANN
5. Result
5.1. Result of Return Loss
5.2. Result of Insertion Loss
6. Conclusion
References